Podcast Episode 47: Davis Cup and Another Week of Tennis Shenanigans

Episode 47 of the Tennis Abstract Podcast, with Carl Bialik of the Thirty Love podcast, takes a look at all things Davis Cup, including the early exit of a Federer-less Swiss team, the unexpected La Liga sponsorship, and the shrinking opportunities for players at ITF events despite all the money that Davis Cup is apparently worth.

We also plow through a list of miscellaneous topics, including a second title for 18-year-old Dayana Yastremska, a career-best final for Donna Vekic, the demise of the Connecticut Open, the persistence of the serve clock, the career slam of Herbert-Mahut, and some new mixed doubles stats.

Thanks for listening!

(Note: this week’s episode is about 65 minutes long; in some browsers the audio player may display a different length. Sorry about that!)

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Unmixing the Gender Gap in Mixed Doubles

Doubles has long been a sort of final frontier in tennis analytics. Double is interesting, at least in part, for the same reason that all team sports are compelling–contributions can come from either player, or a combination of the two. From an analytics perspective, that poses a challenge: Can we isolate what each player brings to the court? I’ve tried to do so with my doubles Elo ratings, but that method relies on players changing partners. It’s not possible to identify how much each half contributed simply by looking at match results.

The problem, as usual, is limited data availability. To know how much value to assign to each player, we need to know what he or she did, even at the basic level of aces, double faults, winners, and errors. The tours report matchstats for many doubles contests, but do not separate the players. Knowing that the Bryan brothers hit 12 aces doesn’t tell us anything about Bob or Mike. The grand slam websites have been better, often providing sequential point-by-point data for some matches, but the same problem persists: They don’t differentiate between players.

That is, until now! The Australian Open website specified the server for each point of every doubles match. (It doesn’t identify the returner on each point, but … baby steps.) That opens up whole new vistas for analytics to separate the contributions of each player.

There’s no I in mixed

A natural place to start is mixed doubles, an event that, due to lack of data, has been almost entirely ignored by analysts. Yet mixed doubles is one of things that everyone seems to have at least a moderate interest in, either because it’s a popular amateur pastime, or because gender differences in sport are inherently fascinating. Due to the variety of skillsets on court at all times, mixed doubles presents tactical puzzles that are different from those posed by same-gender matches.

Let’s start with the basics. There are only 32 teams in a grand slam mixed doubles event, so it’s possible to extend the dataset even further by manually recording which players returned from which sides. (Thanks to Jeff M for a big assist with this.) Thus, for over 3,000 points, we have the gender of the server and the returner. The following table shows several aggregates: Overall mixed doubles averages, typical performance for male and female servers, and rates for male and female returners, including serve points won, first-serve-in rates, and average first serve speed:

Subset           Hold%    SPW  First In  Avg 1st  
Average          76.0%  63.3%     66.2%    103.1  
Men serving      78.6%  65.1%     65.0%    110.2  
Women serving    72.4%  61.3%     67.6%     94.9  
Men returning        -  60.4%     64.6%    103.5  
Women returning      -  65.9%     67.6%    102.8

I was a bit surprised by how narrow the gap is between men and women serving. In men’s doubles at the Australian Open, servers won 67.8% of points, and in women’s doubles, servers won 58.5%. The pool of players is very similar, but in the mixed event, men won fewer serve points and women won more.

Perhaps there is more insight to be gained by looking at more specific matchups:

Server  Returner    SPW  First In  Avg 1st  
Male      Male    61.7%     63.5%    111.0  
Male      Female  68.1%     66.3%    109.5  
Female    Male    58.9%     66.0%     94.6  
Female    Female  63.3%     69.0%     95.1

Tactics appear to change a bit depending on the gender of the returner. Both men and women land more first serves when facing a female returner. However, first serve speed doesn’t vary much. This suggests that David Marrero–who got himself in hot water by possibly fixing a 2016 Australian Open mixed match and then making some questionable comments about inter-gender competition afterward–is unusual in his reluctance to hit hard against female opponents.

Interestingly, the averages from same-gender doubles matches pop up in this table. When men serve to women in mixed doubles, they win 68.1% of points, almost exactly the same rate of serve points won in men’s doubles. When women serve to men, they take 58.9% of points, just a bit higher than the usual rate in women’s doubles. This suggests that while the server-returner matchup is important, the gender of the net player is a key factor as well.

Beware of Melichar

Individual player results against each gender will tell us more, but a single tournament worth of no-ad, third-set super-tiebreak matches doesn’t give us a lot of data on many players. Many members of first-round losing teams served only 20-25 points each. Of the finalists, John Patrick Smith had the biggest gender gap, winning 54.9% of service points against men and 74.4% against women, and his opponent Barbora Krejcikova was similar, winning 59.6% against men and 73.0% against women. Their partners, Astra Sharma and Rajeev Ram, both had narrower gaps of just a few percentage points.

Over the course of the entire event, Sharma was the best server of the four, winning 69.7% of total service points compared to Ram’s 69.0%. But neither came close to semi-finalist Nicole Melichar, who won a whopping 78.4%, narrowly besting her partner, Bruno Soares, who won 77.7%. The Melichar/Soares duo appears to be particularly effective as a unit: Melichar won only 72.6% of service points in her three women’s doubles matches, and Soares won only 70.2% in his men’s doubles quarter-final run alongside Jamie Murray.

The first step toward analyzing any sporting event is simply understanding what’s going on. In the case of mixed doubles, a big part of that is getting a sense of the gender gap on both serve and return. There’s still a painful dearth of data–we now have a mere 31 matches with servers and returners identified for each point–but the next time you watch a mixed doubles match, you’ll be that much smarter about what to expect and what sorts of performances are worthy of further study.

Another Slam, Another Pointless Serve Clock

Italian translation at settesei.it

The 25-second serve clock has quickly become a regular feature on the ATP and WTA tours. After a few trials, it made a debut in the run-up to last year’s US Open, and has become broadly accepted since. The US Open and Australian Open both used the countdown timer, and the WTA will employ the devices at 2019 Premier events, with an eye toward the full slate of tournaments in 2020.

As I understand it, the goal of the serve clock is twofold: First, to keep matches shorter by holding players to a standard time limit between points; and second, to enforce that time limit fairly. Tennis and broadcasting execs are always looking for ways to make matches shorter (or, at least, more predictable in length), so the first goal fits in with broader aims. The second is more specific. Many of the players best known for using a long time between points are big stars, and umpires were thought to be reluctant to penalize them. In theory, a standardized serve clock should make enforcement more transparent and ensure fairness.

The success of the second goal is difficult to assess. In one regard, it seems to be working, because we haven’t heard many players complaining about the system. Progress toward the first goal is much easier to judge, and I’ve done so three times: Once after the 2018 Rogers Cup, once after the joint event in Cincinnati, and a third time following the US Open. Each time, the conclusion was clear: The serve clock did not speed up play, and in many cases, it coincided with slower matches.

Count down under

The simplest way to measure the speed of a tennis match is to use the official match time and number of points played, then calculate the number of seconds per point. It’s a crude technique, since the official match time includes time spent playing, pauses between points, changeovers, heat breaks, medical time outs, challenges, and short rain delays. It’s imperfect. But the time spent on changeovers and the like is usually fairly consistent, making comparisons possible.

Here is the average seconds per point for men and women at the 2018 and 2019 Australian Open, reflecting the pace of play both before and after the introduction of the serve clock:

Year  Men Sec/Pt  Women Sec/Pt  
2018        40.2          40.4  
2019        41.0          40.3

This doesn’t exactly constitute a ringing endorsement of the serve clock. On average, matches were a bit slower in 2019 than in 2018. On the other hand, it’s a better result than the 2018 US Open, which was about 2.5 seconds slower than the 2017 pre-serve clock edition.

More precision, still rather slow

As I said, this is a crude way of measuring match speed. For most tournaments, it’s the best we can do without access to proprietary data that the ATP and WTA (presumably) possess. But at the majors, more detailed information is available. At the US Open, and at the Australian Open until 2017, that was the IBM “Slamtracker” data. The Australian Open no longer works with IBM, but it displays similar point-by-point data on its website.

Armed with better data, we can offer more precise estimates of how often players have exceeded the 25-second limit, both before and after the introduction of the serve clock. (Before the timer, the official limit at slams was 20 seconds, but I don’t think that a single time violation was assessed before at least 25 seconds–or more–had elapsed.) After the US Open last year, I found the number of times that players exceeded 25 seconds increased dramatically, as did the frequency that they went over 30 seconds. If you’re interested, went into more methodological detail in that article.

Again, the Australian Open fares better than its American counterpart, but that doesn’t exactly mean the clock is working, just that it isn’t dramatically slowing things down. Here are some figures from the 2017 and 2019 Australian Opens (I didn’t collect the relevant data last year), showing how often players violated the time limit both before and after the introduction of the timer:

Time Between   2017   2019  Change (%)  
under 20s     77.6%  75.9%       -2.2%  
under 25s     91.6%  91.8%        0.2%  
over 25s       8.4%   8.2%       -1.7%  
over 30s       2.8%   2.1%      -25.2%

The last row of this table is the first point I’ve seen that indicates the serve clock is working. Players are exceeding 30 seconds between points far less often than they did two years ago. On the other hand, there’s almost no difference in how often they cross the 25-second mark. And another negative: The “improved” figure of 2.1% of points over 30 seconds is considerably worse than the same rate in New York last year, which was a mere 0.8%. The clock has eliminated some of the most egregious offenses in Melbourne, but a lot more remain.

Carpenters, not tools

The main problem continues to be the way the serve clock is used. The countdown begins when the score is called, and umpires generally wait until crowd noise has subsided before making their announcement. Thus, after exciting shots or long rallies–the very points after which players have historically taken a long time to serve–the time limit is effectively extended. There’s simply no reason for this. Start the timer when the point is over, and if the crowd is still going wild 20 or 25 seconds later, make the appropriate adjustments. But many servers are already playing “to” the serve clock, using all the time they are allotted. The longer the umpire waits to start the clock, the longer all of us must wait until play resumes.

My primary complaint with delayed clock-starting, though, is a different one. Yes, I’d like matches to move along faster. But as with just about every line in the rulebook, the time limit ends up being extended for stars more than it is for journeymen. On a stadium court like Rod Laver Arena, a modest ovation follows nearly every point played, especially those won by a big name like Federer, Nadal, or Serena. Out on Court 20, Johanna Larsson can play a bruising rally and earn nothing more than a polite golf clap. The more anonymous the player, the less recovery time. After a couple of matches, that adds up. A rule designed to increase fairness and transparency shouldn’t work against unknowns, but in this case, at majors, it appears to do just that.

Eventually, I may stop writing about the serve clock. But as long as the tours are pushing an innovation that fails to meet its stated goals, I’ll keep auditing the results. Given a few more years, maybe they’ll get it right.

Novak Djokovic and the Narrowing Slam Race

Italian translation at settesei.it

It doesn’t take a statistician, or even a spreadsheet, to recognize that the 2019 Australian Open wasn’t Novak Djokovic’s most difficult path to a major title. We can debate whether the straight-set win over Rafael Nadal in the final was due to Djokovic’s utter dominance or a subpar performance from (a possibly still recovering) Rafa. But there’s more to a grand slam title than the final, and the only top-18 opponent Novak faced in the first six rounds was Kei Nishikori, who retired after 52 minutes.

On the traditional grand slam leaderboard, quality of competition doesn’t matter. Roger Federer has 20, Nadal has 17, and now Djokovic has 15. As I’ve written before, the race is closer than that, since Nadal’s and Djokovic’s opponents have, on average, been stronger than Federer’s. My metric for “adjusted slams” estimates the likelihood that a typical major titlist would defeat the specific seven opponents that a player faced, based on their surface-weighted Elo at the time of the match. (I’ve also used this approach for Masters titles.) The explanation is a mouthful, but the underlying idea is simple: Some majors represent greater achievements than others, both because some eras offer stiffer competition and because some draws are particularly daunting.

A slam title against an average level of competition is worth exactly 1. Tougher paths are worth more than 1, and easier draws are worth less. Here is the current leaderboard, with each player’s raw tally, average difficulty rating of their titles, and adjusted total:

Player          Slams  Avg Diff  Adj Slams  
Roger Federer      20      0.88       17.7  
Rafael Nadal       17      1.01       17.1  
Novak Djokovic     15      1.11       16.6

(The numbers in this post do not all precisely agree with those I’ve published in the past, because I’ve improved the accuracy of my Elo-based rating system. All three of the players have seen their adjusted slam totals decrease, because the improved Elo algorithm eliminates some of the Elo “inflation” that overvalued recent achievements.)

These three guys have often had to go through each other, but Djokovic has had the toughest paths of all. The average difficulty of his first 12 majors was 1.2, higher than all but three of Rafa’s titles, one of Roger’s, and two of those won by Pete Sampras. Only recently has he been able to boost his total without quite so much of a challenge. His Australian Open title was worth 0.84 majors, only the fourth of his titles against a below-average set of opponents. It was, however, tougher than Wimbledon or the US Open last year, which were worth 0.77 and 0.65, respectively.

It’s unlikely, of course, that the current leaderboard–adjusted or otherwise–will be the final reckoning among these three men. But on the adjusted list, they will probably remain tightly packed. Because the rest of the pack has weakened, with Andy Murray and Stan Wawrinka no longer regular features of the second week, major titles aren’t what they used to be. Early in the decade, it wasn’t uncommon for a player to beat multiple members of the big four en route to a title and add at least 1.2 to his adjusted tally.

In 2018, slam difficulty was barely half of that recent peak level:

Year    Avg Diff  
2002        0.73  
2003        0.65  
2004        0.82  
2005        0.95  
2006        0.77  
2007        0.93  
2008        1.05  
2009        1.00  
2010        0.95  
2011        1.19  
2012        1.23  
2013        1.22  
2014        1.28  
2015        1.12  
2016        1.27  
2017        0.91  
2018        0.69

This could all change, especially if Djokovic wins a Roland Garros title by upsetting Nadal. (Nothing generates high competition-adjusted numbers like beating Nadal on clay.) But it’s more likely that these three men will have to keep incrementing their totals by 0.6s and 0.7s. While that could be enough to put Rafa or Novak on top by the end of the 2019, it won’t give anyone a commanding lead. It’s a good thing that there’s a lot more to the GOAT debate than slam totals, because slam totals–when properly adjusted for the difficulty of achieving them–make it awfully hard to pick a winner.

Australian Open Coverage at The Economist

I wrote three pieces for the Economist’s Game Theory blog in the last week. The most recent was on Novak Djokovic, who has been dominant on hard courts, but whose few hiccups have come mostly against young players:

Mr Medvedev [followed] a path blazed by Mr Tsitsipas. The Greek prospect allowed Mr Djokovic to hit backhands at a typical 46% clip. But by hitting harder, riskier shots to that side of his opponent, he took Mr Djokovic’s down-the-line weapon out of the game. Mr Djokovic typically sends about one-seventh of his backhands up the line, but against Mr Tsitsipas last summer, that number was cut in half, and Mr Djokovic failed to record a single winner in that direction. In the Melbourne final, Mr Nadal allowed the world’s top-ranked player far more freedom: Mr Djokovic hit one in five of his backhands down the line, and a quarter of those shots ended the point in his favour. Only once has Mr Nadal held his rival’s down-the-line rate below 10%: the 2013 US Open final, the last time the Spaniard got the better of one of their hard-court duels.

After the women’s final, I looked at Naomi Osaka’s accomplishments in comparison to other players in history who were so much younger than tour average. She fares very well by that measure:

Few women have achieved as much as Ms Osaka while being so much younger than tour members as a group. The average age of the top 50 is about 27, nearly six years older than the back-to-back major winner. Only four other players since 1985 have won majors while they were at least 5.5 years younger than the mean of their peers: Ms Williams, Martina Hingis, Maria Sharapova, and Jelena Ostapenko, who won the 2017 French Open but failed to maintain her place in the top ten. None of those players matched Ms Osaka’s feat of following her first grand slam championship by winning another at the first opportunity, and only Ms Hingis claimed her second grand slam within a year of her first. It is too much to predict of any young player that she match the career accomplishments of Ms Williams, whose big-serving style Ms Osaka emulates. But even matching the more modest feats of Ms Hingis and Ms Sharapova, who are tied with five slams apiece, would rank her among the all-time greats.

Finally, I covered Karolina Pliskova’s monumental quarter-final comeback against Serena Williams. There are few, if any, precedents for such a momentum shift in the modern era:

Because collecting point-by-point data for tennis matches is a fairly modern practice, we cannot know for sure where this turnaround ranks in the sport’s long history. But among the 2,300-odd women’s contests that have been manually recorded by volunteers for the Match Charting Project, an online repository of tennis data, there is no example of a greater collapse. Most of the project’s sample is composed of high-profile matches from the 21st century, but there are also a handful of grand-slam duels of yore. Tennis’s most notorious choking incident—when Jana Novotna seemingly lost the ability to hit the ball against Steffi Graf in the 1993 Wimbledon final, after serving for game point at 4-1 in the deciding set—looks unremarkable when compared to Ms Williams’ downfall, with a peak win probability of 95.6%.

Go read them all:

Podcast Episode 46: Australian Open Recap

Episode 46 of the Tennis Abstract Podcast, with Carl Bialik of the Thirty Love podcast, focuses on the eye-popping achievements of Australian Open champs Naomi Osaka and Novak Djokovic. With Osaka, we consider how much she has accomplished in a single year, and whether she has distanced herself from the WTA pack. With Djokovic, we wonder how anyone could ever beat him on a hard court, and how high he’ll climb on the all-time grand slam leaderboard.

Thanks for listening!

(Note: this week’s episode is about 66 minutes long; in some browsers the audio player may display a different length. Sorry about that!)

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The Impact of Rafael Nadal’s New Serve

Italian translation at settesei.it

A couple of years ago, the story of the Australian Open was a certain veteran Swiss player’s new backhand. Roger Federer won the tournament, raced back up the rankings, and eventually reclaimed the No. 1 spot. This season has kicked off with another superstar, Rafael Nadal, attempting to shore up his own relative weakness by streamlining his serve.

The early results are extremely positive. Through the semi-final, Nadal’s first serves in Melbourne have averaged 115 mph, compared to 110 mph at the US Open last fall. He hasn’t been broken in five straight matches, dating back to the second round, and has faced only 13 break points in his last 15 sets. True, he hasn’t faced a truly tough test, as the draw has handed him only two seeds, neither in the top ten. But his lopsided results thus far could equally be ascribed to his own dominance. After all, he demolished Stefanos Tsitsipas only a few days after the Greek prospect ousted Federer.

Serve speed numbers are encouraging and lopsided wins are great for the body, but our focus should always be on points, and how many of them he’s winning. By that measure, Rafa’s retooled serve has excelled, helping the Spaniard post some of the best-ever serving numbers of his grand slam career.

In six matches, Nadal has won 80.9% of his first-serve points. (Fellow finalist Novak Djokovic has won 77.5% of his. Both numbers are outstanding, as the hard-court tour average is below 75%, a figure that includes the contributions of much more dominant servers.) At hard and grass court grand slams, Rafa has done better only twice: 83.6% at the 2010 US Open and 81.3% and Wimbledon in 2008. Here are his top-ten first-serve performances through the semi-finals at hard court majors:

Tournament            1st W%  2nd W%            
2010 US Open           83.6%   66.9%            
2008 Wimbledon         81.3%   64.3%            
2019 Australian Open   80.9%   58.0%            
2013 US Open           79.5%   64.7%            
2017 Wimbledon         79.4%   58.6%            
2011 Wimbledon         79.4%   59.4%            
2010 Wimbledon         79.3%   61.6%            
2006 Wimbledon         77.9%   62.1%            
2012 Wimbledon         77.3%   61.5%            
2012 Australian Open   76.8%   56.7%

You might notice a pattern at the top of this list: Those are slams that he went on to win. The 2010 US Open was his first hard court major title, sealed with a four-set win over Djokovic, his most dominant non-clay victory over his long-time rival. 2008 Wimbledon was his first title there, in the memorable final against Federer. The 2013 US Open was another relatively tidy triumph over Djokovic. All the Wimbledons that clutter the bottom half of this list are inflated a bit by the surface, and it is revealing that Rafa’s next-best performance at the Australian Open sits so far down the list, with his 76.5% first-serve mark in 2012. That fortnight didn’t end in his favor, but it took nearly six hours for Djokovic to beat him.

This is all encouraging and, at the very least, it will make for an interesting aspect of tomorrow’s final, between the newly dangerous serving of Nadal and the ever-brilliant return game of Djokovic. But with only six matches on record, it’s tough to push the analysis much further. Rafa was dominant against Tsitsipas, but barely better than he was against the Greek when they met in Canada last summer. In Australia, he won 80.3% of service points, including 85% of his firsts; in their previous meeting, he won 78.9% of service points and 93.8% of his firsts. A more positive comparison is between his fourth-round win over Tomas Berdych (75.3% service points won, 80.4% firsts) and his previous hard court meetings with the Czech (66.6%, 72.7%). On the other hand, they hadn’t played since 2015 and Berdych is returning from injury, so we can’t put too much weight on the comparison.

Nadal’s more pessimistic fans will be keeping an eye on his second serve in Sunday’s final, as that delivery has not demonstrated the same jump in effectiveness. In the six Melbourne matches, Rafa has won 58.0% of second-serve points, just barely above his career average of 57.3% at hard court majors. That relative weakness was exploited by Alex De Minaur, the best returner of his Aussie Open opponents, who held Nadal to a measly 36.4% of second serve points won. Djokovic is even better, neutralizing bigger second-serve weapons than Rafa’s, so it remains a concern.

If Nadal wins the title, his new serve will rightfully take much of the credit. Not only has it improved his effectiveness on that side of the ball, it has helped keep his matches short and his body ready for the challenges of hard court tennis. Years ago, I bucked the conventional wisdom and argued that Rafa could reach 17 slams. Since then, Federer has shifted the goalposts, but a bigger-serving Nadal makes 20 or 21 look more realistic than ever before.

Petra Kvitova’s Current Status: Low Risk, High Reward

Italian translation at settesei.it

For more a decade, Petra Kvitova has been one of the most aggressive women in tennis. She aims for the corners, hits hard, and lets the chips fall where they may. Sometimes the results are ugly, like a 6-4 6-0 loss to Monica Niculescu in the 2016 Luxembourg final, but when it works, the rewards–two Wimbledon titles, for starters–more than make up for it.

She’s currently riding another wave of winners. Her 11-match win streak–which has involved the loss of only a single set–puts her one more victory away from a third major championship. The 28-year-old Czech has gotten this far by persisting with her big-hitting style, but with a twist: In Melbourne, she’s not missing very often. While she’s ending as many points as ever on her own racket, she’s missing less often than many of her more conservative peers.

In her last five matches at the Australian Open, from the second round through the semi-finals, 7.9% of her shots (including serves) have resulted in unforced errors. In the 88 Petra matches logged by the Match Charting Project, that’s the stingiest five-match stretch of her career. In charted matches since 2010, the average WTA player hits unforced errors on 8.0% of their shots. So Kvitova, the third-most aggressive player on tour, is somehow making errors at a below-average rate. It’s high-risk, high-reward tennis … without the risk.

And it isn’t because her go-for-broke tactics have changed. In Thursday’s semi-final against Danielle Collins, her aggression score–an aggregate measure of point-ending shots including winners, induced forced errors, and unforced errors–was 30.5%, the third-highest of all of her charted matches since her 2017 return to the tour. Her overall aggression score in Melbourne, 28.2%, is also higher than her career average of 27.1%.

In other words, she’s making fewer errors, and the missing errors are turning into point-ending shots in her favor. The following graph shows five-match rolling averages of winners (and induced forced errors) per shot and unforced errors per shot for all charted matches in Kvitova’s career:

Even with the winner and error rates smoothed out by five-match rolling averages, these are still some noisy trend lines. Still, some stories are quite clear. This month, Kvitova is hitting winners at close to her best-ever rate. Her average since the second round in Melbourne has been 20.3%, as high as anything she’s posted before with the exception of her 2014 Wimbledon title. (I’ve never tried to adjust winner totals for surface; it’s possible that the difference can be explained entirely by the grass.)

And most strikingly, this is as big a gap between winner rate and error rate as she’s achieved since her 2014 Wimbledon title run. In fact, between the second round and semi-finals at that tournament, she averaged 8.1% errors and 20.0% winners. Both of her numbers in Australia this year have been a tiny bit better.

Best of all, the error rate has–for the most part–seen a steady downward trend since 2016. The recent error spike is largely due to her three losses in Singapore last October and a bumpy start to this season in Brisbane. We can’t write those off entirely–perhaps Kvitova will always suffer through weeks when her aim goes awry–but she appears to have put them solidly behind her.

None of this is a guarantee that Petra will continue to avoid errors in Saturday’s final against Naomi Osaka. I could’ve written something about her encouraging error rates before the tour finals in Singapore last fall, and she failed to win a round-robin match there. And Osaka is likely to offer a stiffer challenge than any of Kvitova’s previous six opponents in Melbourne, even if her second serve doesn’t. That said, a stingy Kvitova is a terrifying prospect, one with the potential to end the brief WTA depth era and dominate women’s tennis.

The Oddity of Naomi Osaka’s Soft Second Serves

Italian translation at settesei.it

Naomi Osaka has quickly risen to the top of the women’s game on the back of some big hitting, especially a first serve that is one of the fastest in the game. Through Thursday’s semi-final, Osaka’s average first-serve speed in Melbourne was 105 mph, faster than all but two of the other women who reached the third round. Even those two–Aryna Sabalenka and Camila Giorgi–barely edged her out, each with average speeds of 106.

Shift the view to second serves, and Osaka’s place on the list is reversed. While Sabalenka’s typical second offering last week was 90 mph and Giorgi’s was 94, Osaka’s has been a mere 78 mph, the fourth-slowest of the final 32. That mark puts her just ahead of the likes of Angelique Kerber and Sloane Stephens, both whose average first serves are nearly 10 mph slower.

Osaka’s 27 mph gap is the biggest of anyone in this group. The next closest is Caroline Wozniacki’s 23 mph gap, between her 102 mph first serve and 79 mph second serve–both of which are less extreme than the Japanese player’s. Expressed as a ratio, Osaka’s average second serve is only 74% the speed of her typical first. That’s also the widest gap of any third-rounder in Melbourne; Wozniacki is again second-most extreme at 77%.

The following table shows first and second serve speeds, along with the gap and ratio between those two numbers, for a slightly smaller group: women for whom the Australian Open published at least four matches worth of serve-speed data:

Player          Avg 1st  Avg 2nd   Gap  Ratio  
Osaka             105.5     78.5  27.0   0.74  
Keys              105.2     85.4  19.7   0.81  
SWilliams         103.8     88.6  15.2   0.85  
Barty             102.0     88.2  13.7   0.87  
KaPliskova        101.9     80.5  21.4   0.79  
Collins           101.2     82.2  19.1   0.81  
Kvitova            99.6     91.6   8.0   0.92  
Muguruza           98.1     82.5  15.6   0.84  
Pavlyuchenkova     97.9     84.5  13.4   0.86  
Sharapova          97.9     89.6   8.2   0.92  
Svitolina          97.6     78.2  19.4   0.80  
Stephens           96.1     75.1  21.0   0.78  
Halep              95.3     80.9  14.4   0.85  
Kerber             94.0     78.4  15.7   0.83

Oddly enough, having such a slow second serve doesn’t seem to be causing any problems. In today’s semi-final against Karolina Pliskova, Osaka won 81% of first serve points and only 41% of second serve points, but her typical performance behind her second serve is better than that. And in this match, both women feasted on the other’s weaker serves: Pliskova won only 32% of her own second serves. (Though to be fair, Pliskova had the second-largest gap of the players listed above. She tends to rely more on spin than speed when her first serve misses.)

Across her six matches, Osaka has won 73.3% of her first serve points and 49.7% of her second serve points–a bit better than the average quarter-finalist in the former category, a very small amount worse than her peers in the latter. The ratio of those two numbers–68%–is almost identical to those of Danielle Collins, Petra Kvitova, Anastasia Pavlyuchenkova, and Serena Williams, all of whom have smaller gaps between their first and second serve speeds. Of the eight quarter-finalists, Kvitova has the smallest speed gap of all, yet the end result is the same as Osaka’s, she’s just a few percentage points better on both offerings.

Here are the first- and second-serve points won in Melbourne for the eight quarter-finalists, along with the ratio of those two figures and each player’s serve-speed ratio from the previous table:

QFist           1SPW%  2SPW%  W% Ratio  Speed Ratio  
Kvitova         77.9%  52.8%      0.68         0.92  
Williams        74.7%  50.0%      0.67         0.85  
Osaka           73.3%  49.7%      0.68         0.74  
Collins         72.5%  50.0%      0.69         0.81  
Barty           70.8%  55.7%      0.79         0.87  
Pliskova        70.5%  50.0%      0.71         0.79  
Pavlyuchenkova  67.0%  44.9%      0.67         0.86  
Svitolina       66.5%  48.1%      0.72         0.80

Clearly, there’s more than one way to crack the final eight. With Kvitova, we have a server who racks up cheap points with angles instead of speed, rendering the miles-per-hour comparison a bit irrelevant. Serena’s results are close to Osaka’s, though she gets there with bit more bite on her second serves. And then there’s Svitolina, who doesn’t serve very hard or that effectively but can beat you in other ways.

Knowing all this, should Osaka hit harder second serves? In extreme cases, like today’s 81%/41% performance against Pliskova, the answer is yes–had she simply hit nothing but first serves and succeeded at the same rate, she would’ve piled up a lot of double faults but won more total points. But the margins are usually slimmer, and as we’ve seen, her second-serve performance isn’t bad, it just might offer room for improvement. Every player is different, but faster is usually better.

A thorough analysis of that question may be possible with the available data, but it will have to wait for another day. In the meantime, Saturday’s final will offer us a glimpse of contrasting styles: Osaka’s powerful first offering and soft second ball, against Kvitova’s angles and placement on both serves. Both my forecast and the betting market see the title match as a close one–perhaps Osaka’s second serve will be the shot that makes the difference.

The Naomi Osaka First-Set Guarantee

Italian translation at settesei.it

Today in the Australian Open quarter-finals, Naomi Osaka recorded a routine victory, beating 6th seed Elina Svitolina 6-4 6-1. She’ll face Karolina Pliskova in tomorrow’s semi-final, and she has a chance to finish the tournament as the top-ranked player in the world.

(See the bottom of this post for updates.)

Osaka’s sprint to the finish line against Svitolina was what we’ve come to expect from the 21-year-old. The Eurosport commentators shared a remarkable stat: The last 59 times Osaka has won the first set, she has gone on to win the match. (On Eurosport during the match, they said 57, making today’s win 58, but I believe they left out a 2017 win by retirement against Heather Watson in which the first set was completed.) The last time she failed to convert a one-set advantage into a victory was the final match of her 2016 season, in Tianjin against Svetlana Kuznetsova.

Of course, winning the first set is a big advantage for anyone. If two players are evenly matched and there’s no momentum effect, the winner of the first set has a 75% chance of finishing the job. In the real world, the woman who takes the first set is usually the superior player, so her odds in the second and third sets are even better still. On the 2018 WTA tour, the player who claimed first set went on to win the match 81.5% of the time.

Even if Osaka’s theoretical odds of converting one-set advantages are even higher, 59 matches in a row is one heck of a feat. Only 15 women have an active streak of 10 or more consecutive first-set conversions, and a mere four hold a running streak of at least 20. In addition to Osaka, Aryna Sabalenka has converted 25 straight first-set victories, Qiang Wang has won 27 in a row, and Serena Williams is ready to pounce as soon as Osaka falters, with a current tally of 51. Serena’s string of consecutive conversions stretches over an even longer span, back to April 2016, in Miami. (Remember who came back to beat her? Svetlana Kuznetsova.)

It’s no surprise to see Serena showing up near the top of this list. After several years of looking up various tennis records and streaks, I’ve discovered a few general rules. First, if you think you’ve found a noteworthy recent achievement, Serena did it better. Second, if it involves brushing aside the tour’s rank and file, Steffi Graf was even better than Serena. And third, no matter how impressive Serena’s and Steffi’s feats, the all-time record will belong to either Chris Evert or Martina Navratilova.

The first-set-conversion streak no different. In addition to her current streak of 51 straight, Serena won 61 in a row in 2002-03. That’s two matches and three places above Osaka, but it’s only 37th on the all-time list. Graf converted first-set advantages for more than twice as long, tallying 126 in a row from 1989 to 1991. As impressive as that is, my third rule holds with a vengeance: Evert converted 220 in a row between 1978 and 1981 to earn top billing on this list. Navratilova comes in second, but with the consolation that she holds third place as well. Martina and Steffi are the only women with multiple triple-digit streaks.

Here are the longest first-set conversion streaks held by players in the top 40. Many of these women put together multiple streaks of 60 or more, and in those cases I’ve listed only their longest:

Rank  Player                   Matches     Span     Notes  
1     Chris Evert                  220  1978-81  + 3 more  
2     Martina Navratilova          172  1982-84  + 5 more  
4     Steffi Graf                  126  1989-91  + 3 more  
6     Monica Seles                 112  1991-93  + 1 more  
7     Mary Joe Fernandez           105  1989-91            
8     Pam Shriver                  105  1986-88            
9     Vera Zvonareva               103  2006-08            
12    Martina Hingis                86  1996-97            
14    Arantxa Sanchez Vicario       85  1992-93            
16    Victoria Azarenka             79  2011-13            
17    Maria Sharapova               77  2010-12  + 1 more  
19    Margaret Court                74  1969-77            
21    Venus Williams                73  1999-01            
22    Sue Barker                    70  1973-78            
23    Evonne Cawley                 69  1978-80  + 1 more  
24    Lindsay Davenport             67  1999-00  + 1 more  
25    Tracy Austin                  67  1979-80            
26    Virginia Wade                 66  1975-78            
28    Gabriela Sabatini             65  1990-91            
30    Andrea Jaeger                 64  1981-82            
33    Claudia Kohde Kilsch          63  1986-87            
34    Kerry Reid                    62  1969-77            
37    Serena Williams               61  2002-03            
39    Anna Chakvetadze              60  2006-07            
40    Naomi Osaka                   59  2017-19  (active)

* Unfortunately all of these numbers come with a huge caveat. My historical WTA database isn’t perfect. I know that there are Evert and Navratilova matches missing, along with a handful of later results. For records like this, a single missing match could mean that Evert really had two streaks of 110 each, or any number of other permutations that would render my all-time list incorrect. So please, take these records as unofficial, and maybe the WTA will query their own–presumably more complete–database to produce a better list.

This is good company for the reigning US Open champion, and it looks even better if we narrow our view to 21st-century players. Only five of the women ahead of her on the list are active, and four of those are winners of multiple majors–another club that the 21-year-old could join this week. Her semi-final opponent, Karolina Pliskova, executed her own history-making comeback against Serena today. But if Pliskova finds herself down a set to Osaka, even she may not be enough of an escape artist to fight back against the best front-runner in women’s tennis.

Update: Osaka finished off the 2019 Australian Open with two more first-set conversions. In both the semi-final against Pliskova and the final against Kvitova, she won the the first set and went on to win in three. Thus, her streak is up to 61 and she has matched Serena’s best.